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Comparison of the performance of stochastic models in forecasting daily dissolved oxygen data in dam-Lake Thesaurus

机译：随机模型在大坝叙词表每日溶解氧数据预测中的性能比较

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摘要

This study presents the development and validation of three different stochastic models on the basis of (a) their efficiency to forecast and (b) their ability to utilize auxiliary environmental information. The three models are ARIMA models, transfer function (TF) models, and artificial neural networks. Four-year (2004-2007) daily measurements of dissolved oxygen at four different depths (1, 20, 40 and 70m) of Thesaurus dam-lake in River Nestos, Eastern Macedonia, Greece, were used to obtain the best models for these time series. For the final selected models, four statistical criteria (mean square error (MSE), rot-mean-square error (RMSE), MAPE, and NSC) were used to evaluate the accuracy of the forecast and to compare the forecasting ability for one step ahead of each approach. For 1- and 20-m depth, the best forecast is obtained by ARIMA models, while for the 40-m depth, TF models gives the best forecast. Finally for the 70-m depth, according to the MSE, RMSE, and NSC statistical criteria, ARIMA models are the best, while for the MAPE, TF models are the best. Further research could be carried out concerning on (a) the comparison of these models with other forecasting ones, (b) the application of forecasting for more than one step ahead (m=2, 3, ...), and (c) the implementation of such models in other deep lakes and the assessment of the comparison between them.
机译：这项研究基于（a）预测效率和（b）利用辅助环境信息的能力，提出了三种不同的随机模型的开发和验证。这三个模型是ARIMA模型，传递函数（TF）模型和人工神经网络。在希腊东部马其顿的内斯托斯河的同义词库水坝湖的四个不同深度（1、20、40和70m）的四年（2004-2007年）每日溶解氧测量中，获得了这段时间的最佳模型系列。对于最终选择的模型，使用四个统计标准（均方误差（MSE），均方误差（RMSE），MAPE和NSC）来评估预测的准确性并比较一个步骤的预测能力领先于每种方法。对于1米和20米的深度，最好的预测是通过ARIMA模型获得的，而对于40米的深度，TF模型可以给出最佳的预测。最后，对于70米的深度，根据MSE，RMSE和NSC统计标准，ARIMA模型是最好的，而MAPE模型是TF模型的。可以对（a）这些模型与其他预测模型的比较，（b）预测应用超过一个步骤（m = 2、3，...）和（c）进行进一步研究这些模型在其他深湖中的实施情况以及它们之间的比较评估。

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